Overview

Dataset statistics

Number of variables16
Number of observations2157
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory255.1 KiB
Average record size in memory121.1 B

Variable types

DateTime1
Categorical5
Numeric10

Warnings

username has constant value "elonmusk" Constant
cashtags has constant value "0" Constant
tweet has a high cardinality: 2157 distinct values High cardinality
video is highly correlated with photosHigh correlation
photos is highly correlated with videoHigh correlation
replies_count is highly correlated with retweets_count and 1 other fieldsHigh correlation
retweets_count is highly correlated with replies_count and 1 other fieldsHigh correlation
likes_count is highly correlated with replies_count and 1 other fieldsHigh correlation
video is highly correlated with photos and 1 other fieldsHigh correlation
photos is highly correlated with videoHigh correlation
replies_count is highly correlated with retweets_count and 2 other fieldsHigh correlation
retweets_count is highly correlated with replies_count and 2 other fieldsHigh correlation
likes_count is highly correlated with video and 3 other fieldsHigh correlation
number of tweets is highly correlated with replies_count and 2 other fieldsHigh correlation
video is highly correlated with photosHigh correlation
photos is highly correlated with videoHigh correlation
replies_count is highly correlated with retweets_count and 1 other fieldsHigh correlation
retweets_count is highly correlated with replies_count and 1 other fieldsHigh correlation
likes_count is highly correlated with replies_count and 1 other fieldsHigh correlation
replies_count is highly correlated with retweets_count and 1 other fieldsHigh correlation
video is highly correlated with photosHigh correlation
retweets_count is highly correlated with replies_count and 1 other fieldsHigh correlation
photos is highly correlated with videoHigh correlation
bins is highly correlated with percent changeHigh correlation
likes_count is highly correlated with replies_count and 1 other fieldsHigh correlation
percent change is highly correlated with binsHigh correlation
hashtags is highly correlated with cashtags and 1 other fieldsHigh correlation
cashtags is highly correlated with hashtags and 2 other fieldsHigh correlation
bins is highly correlated with cashtags and 1 other fieldsHigh correlation
username is highly correlated with hashtags and 2 other fieldsHigh correlation
tweet is uniformly distributed Uniform
date has unique values Unique
tweet has unique values Unique
mentions has 1944 (90.1%) zeros Zeros
video has 1668 (77.3%) zeros Zeros
photos has 1706 (79.1%) zeros Zeros
urls has 1609 (74.6%) zeros Zeros

Reproduction

Analysis started2021-09-27 19:02:25.376573
Analysis finished2021-09-27 19:02:43.719925
Duration18.34 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

date
Date

UNIQUE

Distinct2157
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
Minimum2016-08-23 16:00:00
Maximum2021-07-20 09:30:00
2021-09-27T15:02:43.909635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:44.065671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

tweet
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct2157
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
Journalist Q&A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time today
 
1
@KMastersBarnes @SpaceX Yeah, most reflights ever! @SciGuySpace Yeah. There was also an early engine shutdown on ascent, but it didn’t affect orbit insertion. Shows value of having 9 engines! Thorough investigation needed before next mission. @EvaFoxU 🤣🤣 Because polygon doesn’t rhyme @annerajb Yeah @PPathole Awe tow co wrecked Abysmal autocorrect might be the #1 reason people don’t fear AI Fear the memesphere @nichegamer Wild times … @j_potoski @AdrianaGalayda @PPathole @1971capital @MLevitt_NP2013 Exactly @nichegamer Seems to be happening a lot @1971capital @MLevitt_NP2013 Very sensible. Knows how to handle exponential functions in reality. @RiganoESQ @DiderRaoult Whether Z-pak works in this situation or not, it’s a kickass med for many maladies https://t.co/UM2TqYpZQZ
 
1
@alvianchoiri @utkarshzaveri @fael097 Yes https://t.co/lzh1uj8QCy @MartianDays Physics is the law, everything else is a recommendation SN3 https://t.co/bM1wzzd4Zg
 
1
@flcnhvy @Tesla Giga New York will reopen for ventilator production as soon as humanly possible. We will do anything in our power to help the citizens of New York. @enscand @PPathole @flcnhvy @Tesla Something weird happened at CDC yesterday. They changed the graph to include “estimated illness onset date”. This is a significantly less rigorous standard. https://t.co/Dz2Nio5ddi @PPathole @flcnhvy @Tesla C19 testing in the US over the past week has grown much faster than C19 positive cases. I think we may have passed the inflection point for US cases (excluding NY) already. @PPathole @flcnhvy @Tesla Yes. Is there more? Should be a lot of data by now. @flcnhvy @Tesla Making good progress. We will do whatever is needed to help in these difficult times.
 
1
This meme proves it https://t.co/3CHAzxv6dj It’s ducked! @SamTalksTesla @MikeBloomberg True. @MikeBloomberg, this is accurate. You believe in journalistic integrity, but if something isn’t done, this will continue. @steezyysosa @TesLatino @flcnhvy @thirdrowtesla @MikeBloomberg @Twitter Have let @twitter know @flcnhvy @thirdrowtesla This is messed up @MikeBloomberg
 
1
Other values (2152)
2152 

Length

Max length6926
Median length274
Mean length475.8701901
Min length1

Characters and Unicode

Total characters1026452
Distinct characters343
Distinct categories18 ?
Distinct scripts7 ?
Distinct blocks16 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2157 ?
Unique (%)100.0%

Sample

1st rowJournalist Q&A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time today
2nd row@Kotaku one of my favorite games as a kid @BelovedRevol Making progress. Maybe something to announce in a few months. Have played all prior Deus Ex. Not this one yet.
3rd rowThanks for the longstanding faith in SpaceX. We very much look forward to doing this milestone flight with you. https://t.co/U2UFez0OhY
4th row@Lockyep Not allowed, according to HK regulations. Happy to do it if regs change. We need to do one more minor rev on 8.0 and then will go to wide release in a few weeks Writing post now with details. Will publish on Tesla website later today. Major improvements to Autopilot coming with V8.0 and 8.1 software (std OTA update) primarily through advanced processing of radar signals @newscientist uh oh
5th rowLoss of Falcon vehicle today during propellant fill operation. Originated around upper stage oxygen tank. Cause still unknown. More soon.

Common Values

ValueCountFrequency (%)
Journalist Q&A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time today1
 
< 0.1%
@KMastersBarnes @SpaceX Yeah, most reflights ever! @SciGuySpace Yeah. There was also an early engine shutdown on ascent, but it didn’t affect orbit insertion. Shows value of having 9 engines! Thorough investigation needed before next mission. @EvaFoxU 🤣🤣 Because polygon doesn’t rhyme @annerajb Yeah @PPathole Awe tow co wrecked Abysmal autocorrect might be the #1 reason people don’t fear AI Fear the memesphere @nichegamer Wild times … @j_potoski @AdrianaGalayda @PPathole @1971capital @MLevitt_NP2013 Exactly @nichegamer Seems to be happening a lot @1971capital @MLevitt_NP2013 Very sensible. Knows how to handle exponential functions in reality. @RiganoESQ @DiderRaoult Whether Z-pak works in this situation or not, it’s a kickass med for many maladies https://t.co/UM2TqYpZQZ1
 
< 0.1%
@alvianchoiri @utkarshzaveri @fael097 Yes https://t.co/lzh1uj8QCy @MartianDays Physics is the law, everything else is a recommendation SN3 https://t.co/bM1wzzd4Zg1
 
< 0.1%
@flcnhvy @Tesla Giga New York will reopen for ventilator production as soon as humanly possible. We will do anything in our power to help the citizens of New York. @enscand @PPathole @flcnhvy @Tesla Something weird happened at CDC yesterday. They changed the graph to include “estimated illness onset date”. This is a significantly less rigorous standard. https://t.co/Dz2Nio5ddi @PPathole @flcnhvy @Tesla C19 testing in the US over the past week has grown much faster than C19 positive cases. I think we may have passed the inflection point for US cases (excluding NY) already. @PPathole @flcnhvy @Tesla Yes. Is there more? Should be a lot of data by now. @flcnhvy @Tesla Making good progress. We will do whatever is needed to help in these difficult times.1
 
< 0.1%
This meme proves it https://t.co/3CHAzxv6dj It’s ducked! @SamTalksTesla @MikeBloomberg True. @MikeBloomberg, this is accurate. You believe in journalistic integrity, but if something isn’t done, this will continue. @steezyysosa @TesLatino @flcnhvy @thirdrowtesla @MikeBloomberg @Twitter Have let @twitter know @flcnhvy @thirdrowtesla This is messed up @MikeBloomberg1
 
< 0.1%
@JBNielsen1985 @stephenpallotta @ajtourville @Teslarati Both @InSpaceXItrust @Kristennetten @thirdrowtesla Could maybe tap the condensation for water too. Seems odd that HVAC systems make pure, fresh water &amp; just dump it on the ground. @SteveHamel16 @JordanWells33 @hereforthecom19 @ScottWapnerCNBC Thanks Tesla China team, China Customs Authority &amp; LAX customs for acting so swiftly @SteveHamel16 @JordanWells33 @hereforthecom19 @ScottWapnerCNBC Yup, China had an oversupply, so we bought 1255 FDA-approved ResMed, Philips &amp; Medtronic ventilators on Friday night &amp; airshipped them to LA. If you want a free ventilator installed, please let us know! @Kristennetten @thirdrowtesla Yeah, pretty much. House could talk to car &amp; know when you’re expected home, so temp &amp; humidity would be perfect just as you arrive. No wasted energy. @romn8tr @stephenpallotta @ajtourville @Teslarati This is a way bigger deal than most people realize @stephenpallotta @ajtourville @Teslarati Sure would love to do home hvac that’s quiet &amp; efficient, with humidity control &amp; HEPA filter @stephenpallotta @ajtourville @Teslarati Yes. PCB design techniques applied to create a heat exchanger that is physically impossible by normal means. Heat pump also has a local heating loop to spool up fast &amp; extend usable temperature range. Octavalve is pretty special too. Team did great work. No credit to me. @ajtourville @Teslarati The heat pump and rear body castings are a step beyond @Teslarati Model Y heat pump is some of the best engineering I’ve seen in a while. Team did next-level work. @justpaulinelol @engineers_feed 🤣🤣 @BocachicaMaria1 Will do @teslaownersSV @RenataKonkoly @sdunbabin @jonkay @Quillette Most likely, imo, we will see a significant reduction in the confirmed C19 case growth rate this week. Follow chart at bottom of this CDC page: https://t.co/vZ3PbhQihG @RenataKonkoly @sdunbabin @jonkay @Quillette Exactly @jonkay @Quillette According to the Italian govt, only 12% of deaths are actually due to C19. This is a significant policy difference in Italy vs most other countries. @engineers_feed Engineering ftw @flyLAXairport Much appreciated1
 
< 0.1%
@PPathole @BBCScienceNews Have you seen more data on HCQ &amp; Z-Pak? Hard to find. @BBCScienceNews Worrisome @thirdrowtesla Yup @JohnnaCrider1 @thirdrowtesla We’ll try to get &amp; deliver as many as possible. N95 masks are a pain to wear btw. Less onerous masks are better most of the time. @Kristennetten @thirdrowtesla @SalehCU Yeah. We have a mask shipment stuck at LAX. Hopefully freed up soon. @engineers_feed Nice @JohnCleese This is great advice @uwdrwaldorf @Tesla @omead_a @UWMedicine Thanks for taking delivery in your garage! Let us know if there’s anything else you need. @justpaulinelol @EvaFoxU @Tesla We expect to have over ~1200 to distribute this week. Getting them delivered, installed &amp; operating is the harder part. @EvaFoxU @Tesla Supply chain logistics — getting masks &amp; other PPE to the right places in time — is the main issue we’re hearing from ER physicians @NativeCACV @Tesla @omead_a You’re most welcome. We’re working on getting other types of PPE too. Ventilators should arrive within a few days.1
 
< 0.1%
@benikbeno @CDCgov Panic is always dumb @Guruleaks1 @benikbeno Italy: “On re-evaluation by the National Institute of Health, only 12 per cent of death certificates have shown a direct causality from coronavirus.” @CDCgov Close contact family gatherings that mix young kids, who have almost no risk of C19 death, with grandparents who have high risk (especially those with prior lung damage), are one of the most powerful mortality vectors1
 
< 0.1%
@Jennerator211 We have N95 masks &amp; getting PAPRs. Will have our team reach out.1
 
< 0.1%
Just had a long engineering discussion with Medtronic about state-of-the-art ventilators. Very impressive team! @SamTalksTesla @flcnhvy @thirdrowtesla @NateSilver538 No problem :) @SamTalksTesla @flcnhvy @thirdrowtesla @NateSilver538 Sigmoid (S-curve) is how all physical and mental viruses behave, regardless of containment. Containment reduces asymptote of S-curve. At this point, we have strict containment in US/Europe &amp; should expect similarly reduced asymptote to China. @flcnhvy @thirdrowtesla @NateSilver538 Sigmoidal for China, followed by sigmoidal for rest of world @Lauren62515251 @PPathole @NateSilver538 Fair point :) @RiccitelliDylan @ColdBuschLights @PPathole @NateSilver538 Exactly, both would be false positive. Also, dying with C19 is different from dying because of C19. Vast majority who died had other illnesses too. https://t.co/YKMW54q2kL @PPathole @NateSilver538 Up to 80% false positive @PPathole @NateSilver538 Sure, although ventilator companies definitely know how to make ventilators. Just a spike in demand right now. Also, using CPAP machines for less severe cases &amp; using one ventilator for several patients seem like good moves to meet short-term demand. @NateSilver538 Do you have data on the false positive rate? I’ve heard a wide range of numbers. CDC numbers are almost an order of magnitude lower between high fidelity data (onset date known) and “presumed positive”. https://t.co/vZ3PbhQihG @NateSilver538 Important consideration1
 
< 0.1%
Other values (2147)2147
99.5%

Length

2021-09-27T15:02:44.423563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
to3519
 
2.3%
the3178
 
2.1%
is2759
 
1.8%
a2718
 
1.8%
of2336
 
1.5%
amp1945
 
1.3%
in1829
 
1.2%
for1593
 
1.0%
tesla1585
 
1.0%
will1328
 
0.9%
Other values (18820)130347
85.1%

Most occurring characters

ValueCountFrequency (%)
152955
14.9%
e85937
 
8.4%
a65732
 
6.4%
t65477
 
6.4%
o58861
 
5.7%
i51540
 
5.0%
s49688
 
4.8%
r49202
 
4.8%
n48414
 
4.7%
l39327
 
3.8%
Other values (333)359319
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter747582
72.8%
Space Separator152955
 
14.9%
Uppercase Letter56415
 
5.5%
Other Punctuation46165
 
4.5%
Decimal Number14404
 
1.4%
Final Punctuation2329
 
0.2%
Connector Punctuation2152
 
0.2%
Other Symbol1459
 
0.1%
Dash Punctuation870
 
0.1%
Close Punctuation575
 
0.1%
Other values (8)1546
 
0.2%

Most frequent character per category

Other Symbol
ValueCountFrequency (%)
🤣424
29.1%
122
 
8.4%
🔥58
 
4.0%
😀48
 
3.3%
🖤41
 
2.8%
🚀38
 
2.6%
😉33
 
2.3%
💕29
 
2.0%
👍24
 
1.6%
🚘17
 
1.2%
Other values (192)625
42.8%
Lowercase Letter
ValueCountFrequency (%)
e85937
11.5%
a65732
 
8.8%
t65477
 
8.8%
o58861
 
7.9%
i51540
 
6.9%
s49688
 
6.6%
r49202
 
6.6%
n48414
 
6.5%
l39327
 
5.3%
h29311
 
3.9%
Other values (41)204093
27.3%
Uppercase Letter
ValueCountFrequency (%)
S6017
 
10.7%
T5996
 
10.6%
A3718
 
6.6%
M3271
 
5.8%
I2986
 
5.3%
C2771
 
4.9%
E2603
 
4.6%
W2485
 
4.4%
P2411
 
4.3%
B2129
 
3.8%
Other values (18)22028
39.0%
Other Punctuation
ValueCountFrequency (%)
@17453
37.8%
.9613
20.8%
,5422
 
11.7%
/4858
 
10.5%
;2039
 
4.4%
&2034
 
4.4%
:1618
 
3.5%
!1483
 
3.2%
?394
 
0.9%
350
 
0.8%
Other values (5)901
 
2.0%
Decimal Number
ValueCountFrequency (%)
02740
19.0%
12412
16.7%
21812
12.6%
31766
12.3%
51179
8.2%
41003
 
7.0%
8938
 
6.5%
9936
 
6.5%
7915
 
6.4%
6703
 
4.9%
Math Symbol
ValueCountFrequency (%)
~273
70.4%
+97
 
25.0%
=12
 
3.1%
2
 
0.5%
2
 
0.5%
1
 
0.3%
1
 
0.3%
Other Letter
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
𖨆1
14.3%
Open Punctuation
ValueCountFrequency (%)
(514
98.5%
[7
 
1.3%
{1
 
0.2%
Close Punctuation
ValueCountFrequency (%)
)567
98.6%
]6
 
1.0%
}2
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
-798
91.7%
47
 
5.4%
25
 
2.9%
Currency Symbol
ValueCountFrequency (%)
$166
98.8%
1
 
0.6%
£1
 
0.6%
Initial Punctuation
ValueCountFrequency (%)
239
97.2%
7
 
2.8%
Final Punctuation
ValueCountFrequency (%)
2087
89.6%
242
 
10.4%
Format
ValueCountFrequency (%)
19
95.0%
1
 
5.0%
Modifier Symbol
ValueCountFrequency (%)
^10
90.9%
🏻1
 
9.1%
Space Separator
ValueCountFrequency (%)
152955
100.0%
Connector Punctuation
ValueCountFrequency (%)
_2152
100.0%
Nonspacing Mark
ValueCountFrequency (%)
184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin803951
78.3%
Common222247
 
21.7%
Inherited203
 
< 0.1%
Cyrillic39
 
< 0.1%
Han6
 
< 0.1%
Greek5
 
< 0.1%
Bamum1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
152955
68.8%
@17453
 
7.9%
.9613
 
4.3%
,5422
 
2.4%
/4858
 
2.2%
02740
 
1.2%
12412
 
1.1%
_2152
 
1.0%
2087
 
0.9%
;2039
 
0.9%
Other values (246)20516
 
9.2%
Latin
ValueCountFrequency (%)
e85937
 
10.7%
a65732
 
8.2%
t65477
 
8.1%
o58861
 
7.3%
i51540
 
6.4%
s49688
 
6.2%
r49202
 
6.1%
n48414
 
6.0%
l39327
 
4.9%
h29311
 
3.6%
Other values (48)260462
32.4%
Cyrillic
ValueCountFrequency (%)
о9
23.1%
в4
10.3%
К3
 
7.7%
р3
 
7.7%
л3
 
7.7%
д2
 
5.1%
к2
 
5.1%
ё2
 
5.1%
и2
 
5.1%
а1
 
2.6%
Other values (8)8
20.5%
Han
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Inherited
ValueCountFrequency (%)
184
90.6%
19
 
9.4%
Greek
ValueCountFrequency (%)
Δ4
80.0%
θ1
 
20.0%
Bamum
ValueCountFrequency (%)
𖨆1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1021709
99.5%
Punctuation3017
 
0.3%
None1045
 
0.1%
VS184
 
< 0.1%
Emoticons162
 
< 0.1%
Misc Symbols161
 
< 0.1%
Enclosed Alphanum Sup70
 
< 0.1%
Cyrillic39
 
< 0.1%
Dingbats25
 
< 0.1%
Latin 1 Sup19
 
< 0.1%
Other values (6)21
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
152955
15.0%
e85937
 
8.4%
a65732
 
6.4%
t65477
 
6.4%
o58861
 
5.8%
i51540
 
5.0%
s49688
 
4.9%
r49202
 
4.8%
n48414
 
4.7%
l39327
 
3.8%
Other values (80)354576
34.7%
Punctuation
ValueCountFrequency (%)
2087
69.2%
350
 
11.6%
242
 
8.0%
239
 
7.9%
47
 
1.6%
25
 
0.8%
19
 
0.6%
7
 
0.2%
1
 
< 0.1%
Dingbats
ValueCountFrequency (%)
16
64.0%
6
 
24.0%
3
 
12.0%
VS
ValueCountFrequency (%)
184
100.0%
Emoticons
ValueCountFrequency (%)
😀48
29.6%
😉33
20.4%
😢9
 
5.6%
😅8
 
4.9%
😍8
 
4.9%
😎6
 
3.7%
😮5
 
3.1%
😜4
 
2.5%
🙏4
 
2.5%
😔3
 
1.9%
Other values (17)34
21.0%
None
ValueCountFrequency (%)
🤣424
40.6%
🔥58
 
5.6%
🖤41
 
3.9%
🚀38
 
3.6%
💕29
 
2.8%
👍24
 
2.3%
🚘17
 
1.6%
💨17
 
1.6%
💫16
 
1.5%
🛸16
 
1.5%
Other values (136)365
34.9%
Latin 1 Sup
ValueCountFrequency (%)
é7
36.8%
ü6
31.6%
ö4
21.1%
£1
 
5.3%
ä1
 
5.3%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🇳11
15.7%
🇴9
12.9%
🇺7
10.0%
🇮7
10.0%
🇸7
10.0%
🇦5
7.1%
🇪5
7.1%
🇯4
 
5.7%
🇵4
 
5.7%
🇩4
 
5.7%
Other values (4)7
10.0%
Misc Symbols
ValueCountFrequency (%)
122
75.8%
8
 
5.0%
8
 
5.0%
6
 
3.7%
6
 
3.7%
2
 
1.2%
2
 
1.2%
1
 
0.6%
1
 
0.6%
1
 
0.6%
Other values (4)4
 
2.5%
Currency Symbols
ValueCountFrequency (%)
1
100.0%
CJK
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Latin Ext A
ValueCountFrequency (%)
ō2
66.7%
ē1
33.3%
Math Operators
ValueCountFrequency (%)
2
33.3%
2
33.3%
1
16.7%
1
16.7%
Letterlike Symbols
ValueCountFrequency (%)
2
50.0%
2
50.0%
Cyrillic
ValueCountFrequency (%)
о9
23.1%
в4
10.3%
К3
 
7.7%
р3
 
7.7%
л3
 
7.7%
д2
 
5.1%
к2
 
5.1%
ё2
 
5.1%
и2
 
5.1%
а1
 
2.6%
Other values (8)8
20.5%
Bamum Sup
ValueCountFrequency (%)
𖨆1
100.0%

username
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
elonmusk
2157 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters17256
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowelonmusk
2nd rowelonmusk
3rd rowelonmusk
4th rowelonmusk
5th rowelonmusk

Common Values

ValueCountFrequency (%)
elonmusk2157
100.0%

Length

2021-09-27T15:02:44.681039image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T15:02:44.751430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
elonmusk2157
100.0%

Most occurring characters

ValueCountFrequency (%)
e2157
12.5%
l2157
12.5%
o2157
12.5%
n2157
12.5%
m2157
12.5%
u2157
12.5%
s2157
12.5%
k2157
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter17256
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2157
12.5%
l2157
12.5%
o2157
12.5%
n2157
12.5%
m2157
12.5%
u2157
12.5%
s2157
12.5%
k2157
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin17256
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2157
12.5%
l2157
12.5%
o2157
12.5%
n2157
12.5%
m2157
12.5%
u2157
12.5%
s2157
12.5%
k2157
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII17256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2157
12.5%
l2157
12.5%
o2157
12.5%
n2157
12.5%
m2157
12.5%
u2157
12.5%
s2157
12.5%
k2157
12.5%

mentions
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1325915624
Minimum0
Maximum9
Zeros1944
Zeros (%)90.1%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2021-09-27T15:02:44.821324image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4825713888
Coefficient of variation (CV)3.639533166
Kurtosis70.42842177
Mean0.1325915624
Median Absolute Deviation (MAD)0
Skewness6.358881745
Sum286
Variance0.2328751453
MonotonicityNot monotonic
2021-09-27T15:02:44.926551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
01944
90.1%
1168
 
7.8%
227
 
1.3%
314
 
0.6%
42
 
0.1%
91
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
01944
90.1%
1168
 
7.8%
227
 
1.3%
314
 
0.6%
42
 
0.1%
51
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
51
 
< 0.1%
42
 
0.1%
314
 
0.6%
227
 
1.3%
1168
 
7.8%
01944
90.1%

hashtags
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
0
2142 
1
 
14
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2157
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02142
99.3%
114
 
0.6%
51
 
< 0.1%

Length

2021-09-27T15:02:45.148091image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T15:02:45.220021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
02142
99.3%
114
 
0.6%
51
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
02142
99.3%
114
 
0.6%
51
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2157
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02142
99.3%
114
 
0.6%
51
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common2157
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02142
99.3%
114
 
0.6%
51
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2157
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02142
99.3%
114
 
0.6%
51
 
< 0.1%

cashtags
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
0
2157 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2157
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02157
100.0%

Length

2021-09-27T15:02:45.407905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T15:02:45.476788image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
02157
100.0%

Most occurring characters

ValueCountFrequency (%)
02157
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2157
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02157
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2157
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02157
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2157
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02157
100.0%

video
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3092257765
Minimum0
Maximum6
Zeros1668
Zeros (%)77.3%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2021-09-27T15:02:45.536943image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6612612362
Coefficient of variation (CV)2.138441509
Kurtosis9.471674108
Mean0.3092257765
Median Absolute Deviation (MAD)0
Skewness2.704869448
Sum667
Variance0.4372664226
MonotonicityNot monotonic
2021-09-27T15:02:45.642961image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
01668
77.3%
1357
 
16.6%
299
 
4.6%
323
 
1.1%
48
 
0.4%
51
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
01668
77.3%
1357
 
16.6%
299
 
4.6%
323
 
1.1%
48
 
0.4%
51
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
51
 
< 0.1%
48
 
0.4%
323
 
1.1%
299
 
4.6%
1357
 
16.6%
01668
77.3%

photos
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3027352805
Minimum0
Maximum7
Zeros1706
Zeros (%)79.1%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2021-09-27T15:02:45.771741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7049105069
Coefficient of variation (CV)2.328471613
Kurtosis15.35249543
Mean0.3027352805
Median Absolute Deviation (MAD)0
Skewness3.297873129
Sum653
Variance0.4968988227
MonotonicityNot monotonic
2021-09-27T15:02:45.877990image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
01706
79.1%
1314
 
14.6%
297
 
4.5%
325
 
1.2%
410
 
0.5%
63
 
0.1%
51
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
01706
79.1%
1314
 
14.6%
297
 
4.5%
325
 
1.2%
410
 
0.5%
51
 
< 0.1%
63
 
0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
63
 
0.1%
51
 
< 0.1%
410
 
0.5%
325
 
1.2%
297
 
4.5%
1314
 
14.6%
01706
79.1%

urls
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3597589244
Minimum0
Maximum6
Zeros1609
Zeros (%)74.6%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2021-09-27T15:02:45.987411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7356629268
Coefficient of variation (CV)2.044877491
Kurtosis10.41605446
Mean0.3597589244
Median Absolute Deviation (MAD)0
Skewness2.768479621
Sum776
Variance0.5411999419
MonotonicityNot monotonic
2021-09-27T15:02:46.089586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
01609
74.6%
1391
 
18.1%
2110
 
5.1%
332
 
1.5%
49
 
0.4%
63
 
0.1%
53
 
0.1%
ValueCountFrequency (%)
01609
74.6%
1391
 
18.1%
2110
 
5.1%
332
 
1.5%
49
 
0.4%
53
 
0.1%
63
 
0.1%
ValueCountFrequency (%)
63
 
0.1%
53
 
0.1%
49
 
0.4%
332
 
1.5%
2110
 
5.1%
1391
 
18.1%
01609
74.6%

replies_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1706
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5082.331015
Minimum6
Maximum204414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2021-09-27T15:02:46.231647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile67
Q1407
median1312
Q34015
95-th percentile22356.4
Maximum204414
Range204408
Interquartile range (IQR)3608

Descriptive statistics

Standard deviation12495.48218
Coefficient of variation (CV)2.458612426
Kurtosis62.6151601
Mean5082.331015
Median Absolute Deviation (MAD)1113
Skewness6.500525765
Sum10962588
Variance156137075
MonotonicityNot monotonic
2021-09-27T15:02:46.420768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1306
 
0.3%
505
 
0.2%
4225
 
0.2%
1585
 
0.2%
4445
 
0.2%
475
 
0.2%
265
 
0.2%
675
 
0.2%
3544
 
0.2%
804
 
0.2%
Other values (1696)2108
97.7%
ValueCountFrequency (%)
61
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
123
0.1%
132
0.1%
182
0.1%
191
 
< 0.1%
201
 
< 0.1%
212
0.1%
ValueCountFrequency (%)
2044141
< 0.1%
1517751
< 0.1%
1445341
< 0.1%
1178941
< 0.1%
1118541
< 0.1%
1043341
< 0.1%
1024551
< 0.1%
896881
< 0.1%
885441
< 0.1%
861261
< 0.1%

retweets_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1870
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13350.84979
Minimum3
Maximum582467
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2021-09-27T15:02:46.595175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile50
Q1605
median3044
Q312054
95-th percentile64299
Maximum582467
Range582464
Interquartile range (IQR)11449

Descriptive statistics

Standard deviation30914.36556
Coefficient of variation (CV)2.315535419
Kurtosis77.88037479
Mean13350.84979
Median Absolute Deviation (MAD)2835
Skewness6.726566076
Sum28797783
Variance955697998.1
MonotonicityNot monotonic
2021-09-27T15:02:46.752757image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
416
 
0.3%
765
 
0.2%
165
 
0.2%
235
 
0.2%
1575
 
0.2%
185
 
0.2%
2354
 
0.2%
294
 
0.2%
564
 
0.2%
284
 
0.2%
Other values (1860)2110
97.8%
ValueCountFrequency (%)
32
0.1%
41
 
< 0.1%
61
 
< 0.1%
72
0.1%
82
0.1%
111
 
< 0.1%
121
 
< 0.1%
131
 
< 0.1%
144
0.2%
151
 
< 0.1%
ValueCountFrequency (%)
5824671
< 0.1%
3771801
< 0.1%
2957021
< 0.1%
2799021
< 0.1%
2758731
< 0.1%
2380141
< 0.1%
2196201
< 0.1%
2189131
< 0.1%
2026971
< 0.1%
1985331
< 0.1%

likes_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2136
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133849.3426
Minimum95
Maximum4727301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2021-09-27T15:02:46.913963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile1099
Q111054
median41308
Q3138600
95-th percentile602881.4
Maximum4727301
Range4727206
Interquartile range (IQR)127546

Descriptive statistics

Standard deviation253783.9651
Coefficient of variation (CV)1.896041924
Kurtosis59.63098605
Mean133849.3426
Median Absolute Deviation (MAD)36566
Skewness5.416606697
Sum288713032
Variance6.440630097 × 1010
MonotonicityNot monotonic
2021-09-27T15:02:47.072638image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10993
 
0.1%
2643
 
0.1%
6082
 
0.1%
28932
 
0.1%
28642
 
0.1%
23982
 
0.1%
92912
 
0.1%
3452
 
0.1%
79992
 
0.1%
4052
 
0.1%
Other values (2126)2135
99.0%
ValueCountFrequency (%)
951
< 0.1%
1261
< 0.1%
1411
< 0.1%
1471
< 0.1%
1491
< 0.1%
1501
< 0.1%
1511
< 0.1%
1601
< 0.1%
1791
< 0.1%
1811
< 0.1%
ValueCountFrequency (%)
47273011
< 0.1%
20405341
< 0.1%
18272501
< 0.1%
17164591
< 0.1%
16591461
< 0.1%
16269531
< 0.1%
16006071
< 0.1%
15828261
< 0.1%
15747231
< 0.1%
15715041
< 0.1%

number of tweets
Real number (ℝ≥0)

HIGH CORRELATION

Distinct37
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.184051924
Minimum1
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2021-09-27T15:02:47.216524image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q37
95-th percentile15
Maximum47
Range46
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.304281683
Coefficient of variation (CV)1.023192237
Kurtosis8.729013126
Mean5.184051924
Median Absolute Deviation (MAD)2
Skewness2.414540454
Sum11182
Variance28.13540417
MonotonicityNot monotonic
2021-09-27T15:02:47.351089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1534
24.8%
2339
15.7%
3219
10.2%
4200
 
9.3%
5163
 
7.6%
6137
 
6.4%
795
 
4.4%
869
 
3.2%
961
 
2.8%
1157
 
2.6%
Other values (27)283
13.1%
ValueCountFrequency (%)
1534
24.8%
2339
15.7%
3219
10.2%
4200
 
9.3%
5163
 
7.6%
6137
 
6.4%
795
 
4.4%
869
 
3.2%
961
 
2.8%
1051
 
2.4%
ValueCountFrequency (%)
471
 
< 0.1%
451
 
< 0.1%
421
 
< 0.1%
391
 
< 0.1%
351
 
< 0.1%
322
0.1%
313
0.1%
302
0.1%
292
0.1%
282
0.1%

price
Real number (ℝ≥0)

Distinct2116
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201.011601
Minimum35.79399872
Maximum891.3800049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2021-09-27T15:02:47.533250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum35.79399872
5-th percentile42.28680038
Q156.59333293
median68.78399658
Q3287
95-th percentile699.6060059
Maximum891.3800049
Range855.5860062
Interquartile range (IQR)230.4066671

Descriptive statistics

Standard deviation234.8994779
Coefficient of variation (CV)1.168586672
Kurtosis0.5824455492
Mean201.011601
Median Absolute Deviation (MAD)20.77399826
Skewness1.43883195
Sum433582.0234
Variance55177.76472
MonotonicityNot monotonic
2021-09-27T15:02:47.833061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8553
 
0.1%
723
 
0.1%
59.347999572
 
0.1%
60.799999242
 
0.1%
55.318000792
 
0.1%
65.555999762
 
0.1%
69.986000062
 
0.1%
63.226001742
 
0.1%
63.200000762
 
0.1%
62.24800112
 
0.1%
Other values (2106)2135
99.0%
ValueCountFrequency (%)
35.793998721
< 0.1%
36.220001221
< 0.1%
36.529998781
< 0.1%
36.555999761
< 0.1%
36.698001861
< 0.1%
36.78599931
< 0.1%
37.004001621
< 0.1%
37.020000461
< 0.1%
37.03200151
< 0.1%
37.047334041
< 0.1%
ValueCountFrequency (%)
891.38000491
< 0.1%
883.09002691
< 0.1%
880.02001951
< 0.1%
870.34997561
< 0.1%
869.66998291
< 0.1%
869.41333011
< 0.1%
861.44665531
< 0.1%
858.02667241
< 0.1%
857.07668051
< 0.1%
8561
< 0.1%

percent change
Real number (ℝ)

HIGH CORRELATION

Distinct2154
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002025802196
Minimum-0.1184687551
Maximum0.1715485869
Zeros3
Zeros (%)0.1%
Negative1008
Negative (%)46.7%
Memory size17.0 KiB
2021-09-27T15:02:48.199039image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1184687551
5-th percentile-0.03610361542
Q1-0.01005215046
median0.001233700095
Q30.01301956268
95-th percentile0.04023603586
Maximum0.1715485869
Range0.290017342
Interquartile range (IQR)0.02307171313

Descriptive statistics

Standard deviation0.02582046493
Coefficient of variation (CV)12.74579768
Kurtosis5.342198596
Mean0.002025802196
Median Absolute Deviation (MAD)0.01157895809
Skewness0.7017843054
Sum4.369655336
Variance0.0006666964092
MonotonicityNot monotonic
2021-09-27T15:02:48.562317image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03
 
0.1%
0.0016198276492
 
0.1%
0.0023181190981
 
< 0.1%
0.012448159051
 
< 0.1%
0.02469366191
 
< 0.1%
-0.024349642861
 
< 0.1%
0.021370859351
 
< 0.1%
-0.015763377421
 
< 0.1%
-0.0071165991111
 
< 0.1%
0.037317964431
 
< 0.1%
Other values (2144)2144
99.4%
ValueCountFrequency (%)
-0.11846875511
< 0.1%
-0.10757228051
< 0.1%
-0.10253249051
< 0.1%
-0.098799827361
< 0.1%
-0.097613024951
< 0.1%
-0.095769383731
< 0.1%
-0.089724354721
< 0.1%
-0.085724448391
< 0.1%
-0.085723568951
< 0.1%
-0.084101975711
< 0.1%
ValueCountFrequency (%)
0.17154858691
< 0.1%
0.15780249351
< 0.1%
0.14128632361
< 0.1%
0.13513728871
< 0.1%
0.13199996951
< 0.1%
0.12761895061
< 0.1%
0.12108615171
< 0.1%
0.12080255451
< 0.1%
0.11471559961
< 0.1%
0.11230359511
< 0.1%

bins
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
no change
1723 
rise
249 
drop
185 

Length

Max length9
Median length9
Mean length7.993973111
Min length4

Characters and Unicode

Total characters17243
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno change
2nd rowno change
3rd rowno change
4th rowno change
5th rowdrop

Common Values

ValueCountFrequency (%)
no change1723
79.9%
rise249
 
11.5%
drop185
 
8.6%

Length

2021-09-27T15:02:48.992438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T15:02:49.096284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
no1723
44.4%
change1723
44.4%
rise249
 
6.4%
drop185
 
4.8%

Most occurring characters

ValueCountFrequency (%)
n3446
20.0%
e1972
11.4%
o1908
11.1%
1723
10.0%
c1723
10.0%
h1723
10.0%
a1723
10.0%
g1723
10.0%
r434
 
2.5%
i249
 
1.4%
Other values (3)619
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15520
90.0%
Space Separator1723
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n3446
22.2%
e1972
12.7%
o1908
12.3%
c1723
11.1%
h1723
11.1%
a1723
11.1%
g1723
11.1%
r434
 
2.8%
i249
 
1.6%
s249
 
1.6%
Other values (2)370
 
2.4%
Space Separator
ValueCountFrequency (%)
1723
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15520
90.0%
Common1723
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n3446
22.2%
e1972
12.7%
o1908
12.3%
c1723
11.1%
h1723
11.1%
a1723
11.1%
g1723
11.1%
r434
 
2.8%
i249
 
1.6%
s249
 
1.6%
Other values (2)370
 
2.4%
Common
ValueCountFrequency (%)
1723
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII17243
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n3446
20.0%
e1972
11.4%
o1908
11.1%
1723
10.0%
c1723
10.0%
h1723
10.0%
a1723
10.0%
g1723
10.0%
r434
 
2.5%
i249
 
1.4%
Other values (3)619
 
3.6%

Interactions

2021-09-27T15:02:28.301725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:28.693874image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:28.810877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:28.939204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:29.058158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:29.184924image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:29.306289image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:29.419737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:29.533090image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:29.647391image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:29.772000image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:29.891618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:30.015694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:30.143846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:30.267646image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:30.404320image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:30.533319image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:30.653818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:30.775931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:30.896186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:31.021744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:31.152567image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:31.283293image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:31.418104image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:31.546859image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:31.688970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:31.823139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:31.947902image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:32.072906image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:32.212380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:32.364084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:32.494815image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:32.626513image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:32.775484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:32.907160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:33.041310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:33.169571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:33.302365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:33.425106image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:33.547122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:33.673232image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:33.808349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:33.944712image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:34.270284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:34.406128image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:34.553625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:34.695173image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:34.829344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:34.962446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:35.093911image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:35.234294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:35.357694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:35.496880image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:35.626426image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:35.755095image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:35.892569image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:36.023821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:36.147873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:36.273655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:36.423054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:36.552077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:36.667497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:36.786796image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:36.909285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:37.029059image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:37.157013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:37.280992image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:37.396799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:37.512979image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:37.636548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:37.758934image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:37.872975image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:37.994616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:38.115651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:38.232781image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:38.360903image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:38.483211image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:38.598415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:38.715393image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:38.829757image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:38.948705image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:39.059975image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:39.176587image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:39.295814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:39.421392image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:39.548575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:39.674727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:39.941329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:40.283624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:40.605069image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:41.024113image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:41.414537image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:41.634075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:41.808009image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:41.982708image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:42.180752image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:42.552651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:42.678714image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:42.801624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:42.923777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-09-27T15:02:49.351983image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-27T15:02:49.598757image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-27T15:02:49.824285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-27T15:02:50.055553image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-27T15:02:50.634837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-27T15:02:43.217701image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-27T15:02:43.576531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

datetweetusernamementionshashtagscashtagsvideophotosurlsreplies_countretweets_countlikes_countnumber of tweetspricepercent changebins
02016-08-23 16:00:00Journalist Q&amp;A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time todayelonmusk000000872464013965244.9679990.002318no change
12016-08-28 09:30:00@Kotaku one of my favorite games as a kid @BelovedRevol Making progress. Maybe something to announce in a few months. Have played all prior Deus Ex. Not this one yet.elonmusk00000068137789244.1626660.011081no change
22016-08-30 16:00:00Thanks for the longstanding faith in SpaceX. We very much look forward to doing this milestone flight with you. https://t.co/U2UFez0OhYelonmusk00000114218397353142.268002-0.022072no change
32016-08-31 16:00:00@Lockyep Not allowed, according to HK regulations. Happy to do it if regs change. We need to do one more minor rev on 8.0 and then will go to wide release in a few weeks Writing post now with details. Will publish on Tesla website later today. Major improvements to Autopilot coming with V8.0 and 8.1 software (std OTA update) primarily through advanced processing of radar signals @newscientist uh ohelonmusk000000391202110165542.4020000.007508no change
42016-09-01 16:00:00Loss of Falcon vehicle today during propellant fill operation. Originated around upper stage oxygen tank. Cause still unknown. More soon.elonmusk0000001159484810743140.153999-0.039424drop
52016-09-02 09:30:00@scrappydog yes. This seems instant from a human perspective, but it really a fast fire, not an explosion. Dragon would have been fine. Finishing Autopilot blog postponed to end of weekendelonmusk0000004265883702240.4660000.007770no change
62016-09-09 09:30:00Will get back to Autopilot update blog tomorrow. @ashwin7002 @NASA @faa @AFPAA We have not ruled that out. @LewisChandlerDN nope, it wasn't me Particularly trying to understand the quieter bang sound a few seconds before the fireball goes off. May come from rocket or something else. Support &amp; advice from @NASA, @FAA, @AFPAA &amp; others much appreciated. Please email any recordings of the event to report@spacex.com. Important to note that this happened during a routine filling operation. Engines were not on and there was no apparent heat source. Still working on the Falcon fireball investigation. Turning out to be the most difficult and complex failure we have ever had in 14 years. @waitbutwhy It's been a little crazy latelyelonmusk3000001761575220453839.8180010.008766no change
72016-09-09 16:00:00@abadcliche Most likely true, but we can't yet find it on any vehicle sensorselonmusk1000003123189138.894001-0.023206no change
82016-09-10 09:30:00Thoughtful Op-ed in Space News much appreciated https://t.co/CJq5g3NIEKelonmusk00000113814003698139.5453340.016746no change
92016-09-10 16:00:00Will do some press Q&amp;A on Autopilot post at 11am PDT tmrw and then publish at noon. Sorry about delay. Unusually difficult couple of weeks.elonmusk0000003218455376139.149334-0.010014no change

Last rows

datetweetusernamementionshashtagscashtagsvideophotosurlsreplies_countretweets_countlikes_countnumber of tweetspricepercent changebins
21472021-07-15 16:00:00@Erdayastronaut @Model3Owners How about a wifi camera link? @BLKMDL3 @Model3Owners In end, we kept production design almost exactly same as show car. Just some small tweaks here &amp; there to make it slightly better. No door handles. Car recognizes you &amp; opens door. Having all four wheels steer is amazing for nimble handling &amp; tight turns! @johnkrausphotos @SpaceX @PortCanaveral Version 3 of the SpaceX droneship. Team did great work! Will be epic to see the deep sea oil rigs converted to ocean spaceports for Starship. @Model3Owners To be frank, there is always some chance that Cybertruck will flop, because it is so unlike anything else. I don’t care. I love it so much even if others don’t. Other trucks look like copies of the same thing, but Cybertruck looks like it was made by aliens from the future. @TesLatino @klwtts @jpr007 Tapering down charge rate is simply a physical thing that has to happen, as lithium ions bounce around what is an increasingly full “parking lot”. Just like a car parking lot, where it takes longer to find a spot when the lot is almost full.elonmusk00000054204030570285650.599976-0.011832no change
21482021-07-16 09:30:00@AustinTeslaClub @SpaceX @austinbarnard45 @PPathole @TeslaOwnersEBay @bluemoondance74 @teslaownersSV @JohnnaCrider1 @TeslaNY Absolutely! @Erdayastronaut @SpaceX Probably Monday @AaronS5_ @FrenchieEAP @karpathy Yes @FrenchieEAP @karpathy FSD beta 9 is using the pure vision production code for highway driving. Beta 10 hopefully (Beta 11 definitely) will use one stack to rule them all – city streets, highway &amp; complex parking lots.elonmusk00000028511771400184654.6799930.006271no change
21492021-07-16 16:00:00@Teslarati Improving permit approval speed &amp; lowering permit costs for solar would make a big difference @TeslaNY Do you even press? @enn_nafnlaus @jpr007 @TesLatino @klwtts Indeed, but again like a parking lot, a battery having big “roads” tends to decrease number of “parking spaces” (ie stores less energy) @RationalEtienne @etherkragg Those are major factorselonmusk00000022361346254844644.219971-0.015977no change
21502021-07-17 09:30:00@techAU Roughly @JeffTutorials @TonyTesla4Life @WholeMarsBlog Yes @TonyTesla4Life @WholeMarsBlog Wide beta maybe with FSD rev 10, definitely with rev 11 @fael097 Pure coincidence! @ValaAfshar Even smaller to a @neuralink chip @facebookai To date, AI chatbots have had a rather short MtH (meantime to Hitler) score. Tay was ~16 hours. https://t.co/FnWMXgpZji @ErcXspace @NASASpaceflight @SpaceX Some of these design trades are still open, but will be resolved soon @ErcXspace @NASASpaceflight @SpaceX Very accurate!elonmusk10000153823775595868646.4166670.003410no change
21512021-07-17 16:00:00@billycrammer @Tesla Cool! @engineers_feed There’s a corner case where brick density is same density as water, reaching bottom due to momentum Fred Astaire is incredible. Worth watching his movies. One of a kind. @TrungTPhan Now, he can bench press a rhino @SamTwits Nice Tap on the screen https://t.co/YPyyj8V8DF @AshleyIllusion1 @lexfridman Lil X is hodling his Doge like a champ. Literally never said the word “sell” even once! @lexfridman “All your basis points are belong to us” - fiat issuers @engineers_feed A classic @riorahardi618 True https://t.co/d4ZOSKZESPelonmusk000220318863025745147011644.886637-0.002367no change
21522021-07-18 09:30:00Cybrrrtruck https://t.co/rdiMFdYOS6 @ArtifactsHub And all-time hodl champion @ValaAfshar Indeed @waitbutwhy Pohtaytohz @squawksquare Current Summon is sometimes useful, but mostly just a fun trick. Once we move summon (plus highway driving) to a single FSD stack, it will be sublime.elonmusk00010018606242212914235638.153341-0.010441no change
21532021-07-18 16:00:00@thePiggsBoson Problem 1st, theory 2nd is for sure way to go, as it establishes relevance, thus improving memory retentionelonmusk00000047525228731645.5533040.011596no change
21542021-07-19 09:30:00@DragTimes @Tesla Nice @WholeMarsBlog You don’t even need to touch the shifter in new S. Auto detect direction will come as an optional setting to all cars with FSD.elonmusk0000001257819193372629.890015-0.024263no change
21552021-07-19 16:00:00@jack @BitcoinMagazine @CathieDWood Sure, I have a ton @BitcoinMagazine @jack @CathieDWood During this talk, we will sing a cover of The Final Countdown by Europe https://t.co/7YUXiW8dhdelonmusk00000119531477226162646.2199710.025925rise
21562021-07-20 09:30:00@vincent13031925 Great to hear! @blueorigin Best of luck tomorrow! @TLPN_Official @SpaceX Depending on progress with Booster 4, we might try a 9 engine firing on Booster 3 Full test duration firing of 3 Raptors on Super Heavy Booster!elonmusk000000702472451450574651.9899900.008929no change